In general, current systems and methods for planning a sequence of errands (e.g., visits to various merchant locations) are highly inefficient as they rely on personal experience and limited information from online resources.
Important decisions in the planning process (e.g., Which merchant locations should be visited? What is the order of visits? How long will each visit take?) are largely made based on personal experiences and a limited amount of information from online sources. For example, online resources such as the Google Maps service provided by Google Inc. of Mountain View, Calif., are useful in identifying locations to visit and routes between locations. However, map services have limited value in that they generally display only current travel/traffic information and are not particularly helpful for accurately determining travel time for a future segment of the trip. Moreover, such services fail to provide any information as to how long it will take to actually visit a particular location. As a result, consumers generally default to planning the sequence of visits and the route based on travel distance and without regard to how long it takes to actually visit a location. Ultimately, because planning a trip and the particular sequence of the visits is based largely on a consumer's limited knowledge, the trips are often not planned effectively and time is often wasted.
Accordingly, there a need for data-driven system and methods for projecting visit durations at various merchant locations and projecting travel times between the merchant locations. Furthermore, it is desirable to have systems and methods for planning an optimized sequence of errands based on the projected durations to merchant locations and travel times. Furthermore it is desirable for such systems and methods for monitoring and updating the optimized sequence to be in real time or near-real time.
It is with respect to these and other considerations that the disclosure made herein is presented.